Density-dependent effects on seston dynamics and rates of filtering and biodeposition of the suspension-cultured scallop Chlamys farreri in a eutrophic bay (northern China): An experimental study in semi-in situ flow-through systems

Effects of stocking density on seston dynamics and filtering and biodeposition by the suspension-cultured Zhikong scallop Chlamys farreri Jones et Preston in a eutrophic bay (Sishili Bay, northern China), were determined in a 3-month semi-field experiment with continuous flow-through seawater from the bay. Results showed that the presence of the scallops could strongly decrease seston and chlorophyll a concentrations in the water column. Moreover, in a limited water column, increasing scallop density could cause seston depletion due to scallop's filtering and biodeposition process, and impair scallop growth. Both filtration rate and biodeposition rate of C. farreri showed significant negative correlation with their density and positive relationship with seston concentration. Calculation predicts that the daily removal of suspended matter from water column by the scallops in Sishili Bay ecosystem can be as high as 45% of the total suspended matter; and the daily production of biodeposits by the scallops in early summer in farming zone may amount to 7.78 g m(-2), with daily C, N and P biodeposition rates of 3.06 x 10(-1), 3.86 x 10(-2) and 9.80 x 10(-3) g m(-2), respectively. The filtering and biodeposition by suspension-cultured scallops could substantially enhance the deposition of total suspended particulate material, suppress accumulation of particulate organic matter in water column, and increase the flux of C, N and P to benthos, strongly enhancing pelagic-benthic coupling. It was suggested that the filtering-biodeposition process by intensively suspension-cultured bivalve filter-feeders could exert strong top-down control on phytoplankton biomass and other suspended particulate material in coastal ecosystems. This study also indicated that commercially suspension-cultured bivalves may simultaneously and potentially aid in mitigating eutrophication pressures on coastal ecosystems subject to anthropogenic N and P loadings, serving as a eutrophic-environment bioremediator. The ecological services (e.g. filtering capacity, top-down control, and benthic-pelagic coupling) functioned by extractive bivalve aquaculture should be emphasized in coastal ecosystems. (c) 2005 Elsevier B.V. All rights reserved.

Influence of filtering and biodeposition by the cultured scallop Chlamys farreri on benthic-pelagic coupling in a eutrophic bay in China

In coastal ecosystems, suspension-cultured bivalve filter feeders may exert a strong impact on phytoplankton and other suspended particulate matter and induce strong pelagic-benthic coupling via intense filtering and biodeposition. We designed an in situ method to determine spatial variations in the filtering-biodeposition process by intensively suspension-cultured scallops Chlamys farreri in summer in a eutrophic bay (Sishili Bay, China), using cylindrical biodeposition traps directly suspended from longlines under ambient environmental conditions. Results showed that bivalve filtering-biodeposition could substantially enhance the deposition of total suspended material and the flux of C, N and P to the benthos, indicating that the suspended filter feeders could strongly enhance pelagic-benthic coupling and exert basin-scale impacts in the Sishili Bay ecosystem. The biodeposition rates of 1-yr-old scallops varied markedly among culture sites (33.8 to 133.0 mg dry material ind.(-1) d(-1)), and were positively correlated with seston concentrations. Mean C, N and P biodeposition rates were 4.00, 0.51, 0.11 mg ind.-1 d-1, respectively. The biodeposition rates of 2-yr-old scallops were almost double these values. Sedimentation rates at scallop culture sites averaged 2.46 times that at the reference site. Theoretically, the total water column of the bay could be filtered by the cultured scallops in 12 d, with daily seston removal amounting to 64%. This study indicated that filtering-biodeposition by suspension-cultured scallops could exert long-lasting top-down control on phytoplankton biomass and other suspended material in the Sishili Bay ecosystem. In coastal waters subject to anthropogenic N and P inputs, suspended bivalve aquaculture could be advantageous, not only economically, but also ecologically, by functioning as a biofilter and potentially mitigating eutrophication pressures. Compared with distribution-restricted wild bivalves, suspension-cultured bivalves in deeper coastal bays may be more efficient in processing seston on a basin scale.

Estimation and Prediction of Evolving Color Distributions for Skin Segmentation Under Varying Illumination

A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and based on predictions of the Markov model. The evolution of the skin color distribution at each frame is parameterized by translation, scaling and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and re-sampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood Estimation, and also evolve over time. Quantitative evaluation of the method was conducted on labeled ground-truth video sequences taken from popular movies.

Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction

British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225)

ART-EMAP: A Neural Network Architecture for Learning and Prediction by Evidence Accumulation

This paper introduces ART-EMAP, a neural architecture that uses spatial and temporal evidence accumulation to extend the capabilities of fuzzy ARTMAP. ART-EMAP combines supervised and unsupervised learning and a medium-term memory process to accomplish stable pattern category recognition in a noisy input environment. The ART-EMAP system features (i) distributed pattern registration at a view category field; (ii) a decision criterion for mapping between view and object categories which can delay categorization of ambiguous objects and trigger an evidence accumulation process when faced with a low confidence prediction; (iii) a process that accumulates evidence at a medium-term memory (MTM) field; and (iv) an unsupervised learning algorithm to fine-tune performance after a limited initial period of supervised network training. ART-EMAP dynamics are illustrated with a benchmark simulation example. Applications include 3-D object recognition from a series of ambiguous 2-D views.

A Neural Network Architecture for Autonomous Learning, Recognition, and Prediction in a Nonstationary World

In a constantly changing world, humans are adapted to alternate routinely between attending to familiar objects and testing hypotheses about novel ones. We can rapidly learn to recognize and narne novel objects without unselectively disrupting our memories of familiar ones. We can notice fine details that differentiate nearly identical objects and generalize across broad classes of dissimilar objects. This chapter describes a class of self-organizing neural network architectures--called ARTMAP-- that are capable of fast, yet stable, on-line recognition learning, hypothesis testing, and naming in response to an arbitrary stream of input patterns (Carpenter, Grossberg, Markuzon, Reynolds, and Rosen, 1992; Carpenter, Grossberg, and Reynolds, 1991). The intrinsic stability of ARTMAP allows the system to learn incrementally for an unlimited period of time. System stability properties can be traced to the structure of its learned memories, which encode clusters of attended features into its recognition categories, rather than slow averages of category inputs. The level of detail in the learned attentional focus is determined moment-by-moment, depending on predictive success: an error due to over-generalization automatically focuses attention on additional input details enough of which are learned in a new recognition category so that the predictive error will not be repeated. An ARTMAP system creates an evolving map between a variable number of learned categories that compress one feature space (e.g., visual features) to learned categories of another feature space (e.g., auditory features). Input vectors can be either binary or analog. Computational properties of the networks enable them to perform significantly better in benchmark studies than alternative machine learning, genetic algorithm, or neural network models. Some of the critical problems that challenge and constrain any such autonomous learning system will next be illustrated. Design principles that work together to solve these problems are then outlined. These principles are realized in the ARTMAP architecture, which is specified as an algorithm. Finally, ARTMAP dynamics are illustrated by means of a series of benchmark simulations.

Digital signal processing and artificial intelligence for the automated classification of food allergy

As a by-product of the ‘information revolution’ which is currently unfolding, lifetimes of man (and indeed computer) hours are being allocated for the automated and intelligent interpretation of data. This is particularly true in medical and clinical settings, where research into machine-assisted diagnosis of physiological conditions gains momentum daily. Of the conditions which have been addressed, however, automated classification of allergy has not been investigated, even though the numbers of allergic persons are rising, and undiagnosed allergies are most likely to elicit fatal consequences. On the basis of the observations of allergists who conduct oral food challenges (OFCs), activity-based analyses of allergy tests were performed. Algorithms were investigated and validated by a pilot study which verified that accelerometer-based inquiry of human movements is particularly well-suited for objective appraisal of activity. However, when these analyses were applied to OFCs, accelerometer-based investigations were found to provide very poor separation between allergic and non-allergic persons, and it was concluded that the avenues explored in this thesis are inadequate for the classification of allergy. Heart rate variability (HRV) analysis is known to provide very significant diagnostic information for many conditions. Owing to this, electrocardiograms (ECGs) were recorded during OFCs for the purpose of assessing the effect that allergy induces on HRV features. It was found that with appropriate analysis, excellent separation between allergic and nonallergic subjects can be obtained. These results were, however, obtained with manual QRS annotations, and these are not a viable methodology for real-time diagnostic applications. Even so, this was the first work which has categorically correlated changes in HRV features to the onset of allergic events, and manual annotations yield undeniable affirmation of this. Fostered by the successful results which were obtained with manual classifications, automatic QRS detection algorithms were investigated to facilitate the fully automated classification of allergy. The results which were obtained by this process are very promising. Most importantly, the work that is presented in this thesis did not obtain any false positive classifications. This is a most desirable result for OFC classification, as it allows complete confidence to be attributed to classifications of allergy. Furthermore, these results could be particularly advantageous in clinical settings, as machine-based classification can detect the onset of allergy which can allow for early termination of OFCs. Consequently, machine-based monitoring of OFCs has in this work been shown to possess the capacity to significantly and safely advance the current state of clinical art of allergy diagnosis

Signal Improvement and Contrast Enhancement in Magnetic Resonance Imaging

This thesis reports advances in magnetic resonance imaging (MRI), with the ultimate goal of improving signal and contrast in biomedical applications. More specifically, novel MRI pulse sequences have been designed to characterize microstructure, enhance signal and contrast in tissue, and image functional processes. In this thesis, rat brain and red bone marrow images are acquired using iMQCs (intermolecular multiple quantum coherences) between spins that are 10 μm to 500 μm apart. As an important application, iMQCs images in different directions can be used for anisotropy mapping. We investigate tissue microstructure by analyzing anisotropy mapping. At the same time, we simulated images expected from rat brain without microstructure. We compare those with experimental results to prove that the dipolar field from the overall shape only has small contributions to the experimental iMQC signal. Besides magnitude of iMQCs, phase of iMQCs should be studied as well. The phase anisotropy maps built by our method can clearly show susceptibility information in kidneys. It may provide meaningful diagnostic information. To deeply study susceptibility, the modified-crazed sequence is developed. Combining phase data of modified-crazed images and phase data of iMQCs images is very promising to construct microstructure maps. Obviously, the phase image in all above techniques needs to be highly-contrasted and clear. To achieve the goal, algorithm tools from Susceptibility-Weighted Imaging (SWI) and Susceptibility Tensor Imaging (STI) stands out superb useful and creative in our system.

A framework for state-based time-series analysis and prediction

Time-series analysis and prediction play an important role in state-based systems that involve dealing with varying situations in terms of states of the world evolving with time. Generally speaking, the world in the discourse persists in a given state until something occurs to it into another state. This paper introduces a framework for prediction and analysis based on time-series of states. It takes a time theory that addresses both points and intervals as primitive time elements as the temporal basis. A state of the world under consideration is defined as a set of time-varying propositions with Boolean truth-values that are dependent on time, including properties, facts, actions, events and processes, etc. A time-series of states is then formalized as a list of states that are temporally ordered one after another. The framework supports explicit expression of both absolute and relative temporal knowledge. A formal schema for expressing general time-series of states to be incomplete in various ways, while the concept of complete time-series of states is also formally defined. As applications of the formalism in time-series analysis and prediction, we present two illustrating examples.

Causes and prediction of abrupt climate-driven ecosystem shifts in the North Atlantic

Warming of the global climate is now unequivocal and its impact on Earth’ functional units has become more apparent. Here, we show that marine ecosystems are not equally sensitive to climate change and reveal a critical thermal boundary where a small increase in temperature triggers abrupt ecosystem shifts seen across multiple trophic levels. This large-scale boundary is located in regions where abrupt ecosystem shifts have been reported in the North Atlantic sector and thereby allows us to link these shifts by a global common phenomenon. We show that these changes alter the biodiversity and carrying capacity of ecosystems and may, combined with fishing, precipitate the reduction of some stocks of Atlantic cod already severely impacted by exploitation. These findings offer a way to anticipate major ecosystem changes and to propose adaptive strategies for marine exploited resources such as cod in order to minimize social and economic consequences.

Modelling and Prediction of NOx Emission in a Coal-Fired Power Generation Plant

In this paper NOx emissions modelling for real-time operation and control of a 200 MWe coal-fired power generation plant is studied. Three model types are compared. For the first model the fundamentals governing the NOx formation mechanisms and a system identification technique are used to develop a grey-box model. Then a linear AutoRegressive model with eXogenous inputs (ARX) model and a non-linear ARX model (NARX) are built. Operation plant data is used for modelling and validation. Model cross-validation tests show that the developed grey-box model is able to consistently produce better overall long-term prediction performance than the other two models.